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The emergence of large language models (LLMs) represents a major advance in artificial intelligence (AI) research. However, the widespread use of LLMs is also coupled with significant ethical and social challenges. Previous research has pointed towards auditing as a promising governance mechanism to help ensure that AI systems are designed and deployed in ways that are ethical, legal, and technically robust. However, existing auditing procedures fail to address the governance challenges posed by LLMs, which are adaptable to a wide range of downstream tasks. To help bridge that gap, Mokander will present a novel blueprint for how to audit LLMs. By drawing on best practices from IT governance and system engineering, he and his collaborators at the University of Oxford propose a three-layered approach, whereby governance audits, model audits, and application audits complement and inform each other. Ultimately, this research seeks to expand the methodological toolkit available to technolog

New delhiLuciano floridiPrinceton university media centralOxford internet instituteSwedish trade invest councilInformation technologyUniversity of oxfordVisiting scholarSocial scienceIndustrial engineeringSwedish tradeInvest councilGoogle scholarJean butcher